In low and middle income countries, household surveys are a valuable source of information for a range of health and demographic indicators. Increasingly, subnational estimates are required for targeting interventions and evaluating progress towards targets. In the majority of cases, stratified cluster sampling is used, with clusters corresponding to enumeration areas. The reported geographical information varies. A common procedure, to preserve confidentiality, is to give a jittered location with the true centroid of the cluster is displaced under a known algorithm. An alternative situation, which was used for older surveys in particular, is to report the geographical region within the cluster lies. In this paper, we describe a spatial hierarchical model in which we account for inaccuracies in the cluster locations. The computational algorithm we develop is fast and avoids the heavy computation of a pure MCMC approach. We illustrate by simulation the benefits of the model, over naive alternatives.
翻译:在中低收入国家,户口调查是一系列健康和人口指标的宝贵信息来源,越来越需要国家以下各级的估计数,以针对干预措施和评价实现目标的进展;在多数情况下,采用分层的群集抽样,按查点区进行分组;所报告的地理信息各不相同;为了保密,一个共同的程序是,根据已知的算法,让一个有该组真正中间体的杂乱地点被移走;一个特别用于老式调查的替代情况是报告该组内的地理区域。我们本文描述了一个空间等级模式,其中我们说明了组别地点不准确的情况。我们开发的计算算法是快速的,避免了纯MCMCM方法的沉重计算。我们通过模拟模型的效益来说明,而不是天真的替代方法。